Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x7f89b005a390>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x7f89aff7ff28>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.0.0
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    real_inputs = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels),name = 'real_inputs')
    z_inputs = tf.placeholder(tf.float32, (None, z_dim), name = 'z_inputs')
    learning_rate = tf.placeholder(tf.float32, None, name = 'learning_rate')

    return real_inputs, z_inputs, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the generator, tensor logits of the generator).

In [6]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param image: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function
    with tf.variable_scope('discriminator',reuse = reuse):
        alpha = 0.01
        #28x28x3
        x1 = tf.layers.conv2d(images, 128, 3, strides=2, padding='same')
        relu1 = tf.maximum(alpha * x1, x1)
        #14x14x
        x2 = tf.layers.conv2d(relu1, 256, 3, strides=2, padding='same')
        bn2 = tf.layers.batch_normalization(x2, training=True)
        relu2 = tf.maximum(alpha * bn2, bn2)
        relu2 = tf.nn.dropout(relu2, 0.8)
        #7x7x
        
        flat = tf.reshape(relu2, (-1, 7*7*256))
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)

    return logits, out


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [7]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function
    with tf.variable_scope('generator', reuse = not is_train):
        alpha = 0.1
        x1 = tf.layers.dense(z, 7*7*1024)
        
        x1 = tf.reshape(x1, (-1, 7, 7, 1024))
        x1 = tf.layers.batch_normalization(x1, training=is_train)
        x1 = tf.maximum(alpha * x1, x1)
        
        x2 = tf.layers.conv2d_transpose(x1, 512, 3, strides=2, padding='same')
        x2 = tf.layers.batch_normalization(x2, training=is_train)
        x2 = tf.maximum(alpha * x2, x2)
        
        x3 = tf.layers.conv2d_transpose(x2, 256, 3, strides=2, padding='same')
        x3 = tf.layers.batch_normalization(x3, training=is_train)
        x3 = tf.maximum(alpha * x3, x3)
        
        #x4 = tf.layers.conv2d_transpose(x3, 128, 3, strides=2, padding='same')
        #x4 = tf.layers.batch_normalization(x4, training=is_train)
        #x4 = tf.maximum(alpha * x4, x4)
        
        logits = tf.layers.conv2d_transpose(x3, out_channel_dim, 3, strides=1, padding='same')
        out = tf.tanh(logits)
    
    return out


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [8]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    g_model = generator(input_z, out_channel_dim, is_train=True)
    d_real_logits, d_real_out = discriminator(input_real, reuse=False)
    d_fake_logits, d_fake_out = discriminator(g_model, reuse=True)
    
    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_real_logits, labels=tf.ones_like(d_real_out)))
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_fake_logits, labels=tf.zeros_like(d_fake_out)))
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_fake_logits, labels=tf.ones_like(d_fake_out)))
    
    d_loss = d_loss_real + d_loss_fake
    
    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [9]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]
    
    # Optimize
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)
    
    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [10]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [11]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model
    real_inputs, z_inputs, lr = model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim)
    d_loss, g_loss = model_loss(real_inputs, z_inputs, data_shape[3])
    d_train_opt, g_train_opt = model_opt(d_loss, g_loss, lr, beta1)
    steps = 0
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                steps += 1
                # TODO: Train Model
                # Sample random noise for G
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                batch_images = batch_images*2
                # Run optimizers
                _ = sess.run(d_train_opt, feed_dict={real_inputs: batch_images, z_inputs: batch_z, lr:learning_rate})
                _ = sess.run(g_train_opt, feed_dict={real_inputs: batch_images, z_inputs: batch_z, lr:learning_rate})
                
                if steps % 100 == 0:
                    show_generator_output(sess, 16, z_inputs, data_shape[3], data_image_mode)
                    train_loss_d = d_loss.eval({z_inputs: batch_z, real_inputs: batch_images*2})
                    train_loss_g = g_loss.eval({z_inputs: batch_z})
                    print("Epoch {}/{}...".format(epoch_i+1, epoch_count),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))
                
                

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [ ]:
batch_size = 32
z_dim = 50
learning_rate = 0.001
beta1 = 0.1


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2... Discriminator Loss: 1.8762... Generator Loss: 2.8474
Epoch 1/2... Discriminator Loss: 1.4878... Generator Loss: 0.4276
Epoch 1/2... Discriminator Loss: 1.2809... Generator Loss: 1.2228
Epoch 1/2... Discriminator Loss: 1.2623... Generator Loss: 1.3281
Epoch 1/2... Discriminator Loss: 2.4388... Generator Loss: 0.1341
Epoch 1/2... Discriminator Loss: 1.3180... Generator Loss: 0.5138
Epoch 1/2... Discriminator Loss: 1.3378... Generator Loss: 0.4931
Epoch 1/2... Discriminator Loss: 1.4265... Generator Loss: 0.4569
Epoch 1/2... Discriminator Loss: 1.3819... Generator Loss: 0.5151
Epoch 1/2... Discriminator Loss: 2.7327... Generator Loss: 0.0937
Epoch 1/2... Discriminator Loss: 2.5181... Generator Loss: 2.1778
Epoch 1/2... Discriminator Loss: 1.4566... Generator Loss: 0.4850
Epoch 1/2... Discriminator Loss: 1.4245... Generator Loss: 0.4356
Epoch 1/2... Discriminator Loss: 4.1633... Generator Loss: 0.0428
Epoch 1/2... Discriminator Loss: 1.0868... Generator Loss: 1.1142
Epoch 1/2... Discriminator Loss: 1.4918... Generator Loss: 0.7916
Epoch 1/2... Discriminator Loss: 0.9310... Generator Loss: 0.8113
Epoch 1/2... Discriminator Loss: 1.0842... Generator Loss: 0.7703
Epoch 2/2... Discriminator Loss: 0.8718... Generator Loss: 1.1814
Epoch 2/2... Discriminator Loss: 0.7807... Generator Loss: 1.2079
Epoch 2/2... Discriminator Loss: 0.9837... Generator Loss: 0.9261
Epoch 2/2... Discriminator Loss: 1.1865... Generator Loss: 0.5694
Epoch 2/2... Discriminator Loss: 1.5249... Generator Loss: 0.3054
Epoch 2/2... Discriminator Loss: 1.0912... Generator Loss: 0.7545
Epoch 2/2... Discriminator Loss: 1.4182... Generator Loss: 0.5539
Epoch 2/2... Discriminator Loss: 1.2633... Generator Loss: 1.3356
Epoch 2/2... Discriminator Loss: 2.0718... Generator Loss: 2.4535
Epoch 2/2... Discriminator Loss: 1.0023... Generator Loss: 1.2490
Epoch 2/2... Discriminator Loss: 1.1790... Generator Loss: 0.7085
Epoch 2/2... Discriminator Loss: 1.2322... Generator Loss: 1.7242
Epoch 2/2... Discriminator Loss: 0.8017... Generator Loss: 1.1420
Epoch 2/2... Discriminator Loss: 0.4594... Generator Loss: 1.5998
Epoch 2/2... Discriminator Loss: 0.6316... Generator Loss: 1.1878
Epoch 2/2... Discriminator Loss: 0.6351... Generator Loss: 1.1834
Epoch 2/2... Discriminator Loss: 0.7804... Generator Loss: 1.0507
Epoch 2/2... Discriminator Loss: 1.2342... Generator Loss: 2.0836
Epoch 2/2... Discriminator Loss: 0.8542... Generator Loss: 1.5664

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [ ]:
batch_size = 32
z_dim = 50
learning_rate = 0.001
beta1 = 0.1


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 5

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/5... Discriminator Loss: 1.7133... Generator Loss: 0.6955
Epoch 1/5... Discriminator Loss: 2.3134... Generator Loss: 0.4980
Epoch 1/5... Discriminator Loss: 2.6203... Generator Loss: 0.4258
Epoch 1/5... Discriminator Loss: 3.7913... Generator Loss: 0.6966
Epoch 1/5... Discriminator Loss: 2.5944... Generator Loss: 0.2276
Epoch 1/5... Discriminator Loss: 5.1610... Generator Loss: 1.1980
Epoch 1/5... Discriminator Loss: 3.7813... Generator Loss: 0.6703
Epoch 1/5... Discriminator Loss: 3.7754... Generator Loss: 0.7208
Epoch 1/5... Discriminator Loss: 4.2187... Generator Loss: 0.7546
Epoch 1/5... Discriminator Loss: 2.4656... Generator Loss: 0.6705
Epoch 1/5... Discriminator Loss: 3.7424... Generator Loss: 0.7866
Epoch 1/5... Discriminator Loss: 2.6515... Generator Loss: 0.5477
Epoch 1/5... Discriminator Loss: 3.4909... Generator Loss: 0.4085
Epoch 1/5... Discriminator Loss: 2.8899... Generator Loss: 0.7638
Epoch 1/5... Discriminator Loss: 2.5435... Generator Loss: 0.7335
Epoch 1/5... Discriminator Loss: 2.5702... Generator Loss: 0.5116
Epoch 1/5... Discriminator Loss: 2.8623... Generator Loss: 0.4809
Epoch 1/5... Discriminator Loss: 1.7970... Generator Loss: 0.6096
Epoch 1/5... Discriminator Loss: 1.4522... Generator Loss: 0.5446
Epoch 1/5... Discriminator Loss: 1.8080... Generator Loss: 0.4774
Epoch 1/5... Discriminator Loss: 2.1012... Generator Loss: 1.0214
Epoch 1/5... Discriminator Loss: 2.2980... Generator Loss: 0.4984
Epoch 1/5... Discriminator Loss: 1.4666... Generator Loss: 0.7154
Epoch 1/5... Discriminator Loss: 2.1623... Generator Loss: 0.6684
Epoch 1/5... Discriminator Loss: 1.6070... Generator Loss: 0.5998
Epoch 1/5... Discriminator Loss: 1.3941... Generator Loss: 0.5883
Epoch 1/5... Discriminator Loss: 2.1261... Generator Loss: 0.5506
Epoch 1/5... Discriminator Loss: 2.2667... Generator Loss: 0.8683
Epoch 1/5... Discriminator Loss: 3.2135... Generator Loss: 0.6049
Epoch 1/5... Discriminator Loss: 3.3333... Generator Loss: 1.3941
Epoch 1/5... Discriminator Loss: 3.5696... Generator Loss: 0.5854
Epoch 1/5... Discriminator Loss: 3.6576... Generator Loss: 0.7324
Epoch 1/5... Discriminator Loss: 3.4257... Generator Loss: 0.3174
Epoch 1/5... Discriminator Loss: 2.9169... Generator Loss: 0.6076
Epoch 1/5... Discriminator Loss: 3.7520... Generator Loss: 0.7569
Epoch 1/5... Discriminator Loss: 3.9245... Generator Loss: 0.8694
Epoch 1/5... Discriminator Loss: 2.5940... Generator Loss: 0.4176
Epoch 1/5... Discriminator Loss: 3.5862... Generator Loss: 0.7658
Epoch 1/5... Discriminator Loss: 3.6540... Generator Loss: 0.7231
Epoch 1/5... Discriminator Loss: 6.6315... Generator Loss: 3.1489
Epoch 1/5... Discriminator Loss: 2.6061... Generator Loss: 0.7208
Epoch 1/5... Discriminator Loss: 3.4955... Generator Loss: 0.7419
Epoch 1/5... Discriminator Loss: 3.8013... Generator Loss: 0.5966
Epoch 1/5... Discriminator Loss: 3.2475... Generator Loss: 0.5439
Epoch 1/5... Discriminator Loss: 2.0850... Generator Loss: 0.5385
Epoch 1/5... Discriminator Loss: 2.5900... Generator Loss: 0.5833
Epoch 1/5... Discriminator Loss: 2.0036... Generator Loss: 0.5677
Epoch 1/5... Discriminator Loss: 2.8138... Generator Loss: 0.5004
Epoch 1/5... Discriminator Loss: 1.4391... Generator Loss: 0.5534
Epoch 1/5... Discriminator Loss: 3.0620... Generator Loss: 0.5953
Epoch 1/5... Discriminator Loss: 3.1386... Generator Loss: 0.7954
Epoch 1/5... Discriminator Loss: 1.5531... Generator Loss: 0.6323
Epoch 1/5... Discriminator Loss: 2.0270... Generator Loss: 0.6235
Epoch 1/5... Discriminator Loss: 1.6329... Generator Loss: 0.4976
Epoch 1/5... Discriminator Loss: 2.1276... Generator Loss: 0.5797
Epoch 1/5... Discriminator Loss: 2.4502... Generator Loss: 0.5921
Epoch 1/5... Discriminator Loss: 2.5124... Generator Loss: 0.5177
Epoch 1/5... Discriminator Loss: 2.1964... Generator Loss: 0.6245
Epoch 1/5... Discriminator Loss: 1.9341... Generator Loss: 0.5153
Epoch 1/5... Discriminator Loss: 5.5715... Generator Loss: 2.3170
Epoch 1/5... Discriminator Loss: 2.8988... Generator Loss: 0.6325
Epoch 1/5... Discriminator Loss: 2.4615... Generator Loss: 0.6425
Epoch 1/5... Discriminator Loss: 2.5054... Generator Loss: 0.6461
Epoch 2/5... Discriminator Loss: 2.4861... Generator Loss: 0.5161
Epoch 2/5... Discriminator Loss: 2.2630... Generator Loss: 0.5171
Epoch 2/5... Discriminator Loss: 2.0823... Generator Loss: 0.5928
Epoch 2/5... Discriminator Loss: 1.6845... Generator Loss: 0.5953
Epoch 2/5... Discriminator Loss: 2.6509... Generator Loss: 0.5660
Epoch 2/5... Discriminator Loss: 1.7396... Generator Loss: 0.5263
Epoch 2/5... Discriminator Loss: 2.6605... Generator Loss: 0.5749
Epoch 2/5... Discriminator Loss: 1.8372... Generator Loss: 0.6081
Epoch 2/5... Discriminator Loss: 2.1854... Generator Loss: 0.6195
Epoch 2/5... Discriminator Loss: 1.9696... Generator Loss: 0.5648
Epoch 2/5... Discriminator Loss: 1.9120... Generator Loss: 0.5081
Epoch 2/5... Discriminator Loss: 1.7289... Generator Loss: 0.6370
Epoch 2/5... Discriminator Loss: 1.5612... Generator Loss: 0.6117
Epoch 2/5... Discriminator Loss: 1.9780... Generator Loss: 0.5406
Epoch 2/5... Discriminator Loss: 1.5859... Generator Loss: 0.6096
Epoch 2/5... Discriminator Loss: 1.9759... Generator Loss: 0.6356
Epoch 2/5... Discriminator Loss: 1.9722... Generator Loss: 0.5605
Epoch 2/5... Discriminator Loss: 2.0905... Generator Loss: 0.6205
Epoch 2/5... Discriminator Loss: 1.7821... Generator Loss: 0.6412
Epoch 2/5... Discriminator Loss: 1.9019... Generator Loss: 0.4734
Epoch 2/5... Discriminator Loss: 1.7327... Generator Loss: 0.5983
Epoch 2/5... Discriminator Loss: 1.7591... Generator Loss: 0.6445
Epoch 2/5... Discriminator Loss: 1.7740... Generator Loss: 0.5439
Epoch 2/5... Discriminator Loss: 1.8199... Generator Loss: 0.5347
Epoch 2/5... Discriminator Loss: 1.6815... Generator Loss: 0.5278
Epoch 2/5... Discriminator Loss: 1.5081... Generator Loss: 0.5854
Epoch 2/5... Discriminator Loss: 1.4529... Generator Loss: 0.5477
Epoch 2/5... Discriminator Loss: 1.6610... Generator Loss: 0.6324
Epoch 2/5... Discriminator Loss: 1.4861... Generator Loss: 0.4724
Epoch 2/5... Discriminator Loss: 1.3483... Generator Loss: 0.6260
Epoch 2/5... Discriminator Loss: 1.9513... Generator Loss: 0.5560
Epoch 2/5... Discriminator Loss: 1.4777... Generator Loss: 0.5243
Epoch 2/5... Discriminator Loss: 1.4807... Generator Loss: 0.7769
Epoch 2/5... Discriminator Loss: 1.6412... Generator Loss: 0.5318
Epoch 2/5... Discriminator Loss: 1.7881... Generator Loss: 0.5625
Epoch 2/5... Discriminator Loss: 1.9660... Generator Loss: 0.5455
Epoch 2/5... Discriminator Loss: 3.0778... Generator Loss: 1.3860
Epoch 2/5... Discriminator Loss: 1.2351... Generator Loss: 0.5932
Epoch 2/5... Discriminator Loss: 1.7205... Generator Loss: 0.5087
Epoch 2/5... Discriminator Loss: 1.6999... Generator Loss: 0.6420
Epoch 2/5... Discriminator Loss: 1.5938... Generator Loss: 0.7086
Epoch 2/5... Discriminator Loss: 1.6592... Generator Loss: 0.5821
Epoch 2/5... Discriminator Loss: 1.8075... Generator Loss: 0.5216
Epoch 2/5... Discriminator Loss: 1.5287... Generator Loss: 0.4608
Epoch 2/5... Discriminator Loss: 1.4430... Generator Loss: 0.5745
Epoch 2/5... Discriminator Loss: 1.8271... Generator Loss: 0.5739
Epoch 2/5... Discriminator Loss: 1.6574... Generator Loss: 0.6151
Epoch 2/5... Discriminator Loss: 1.4307... Generator Loss: 0.5515
Epoch 2/5... Discriminator Loss: 1.6256... Generator Loss: 0.5536
Epoch 2/5... Discriminator Loss: 1.5077... Generator Loss: 0.6824
Epoch 2/5... Discriminator Loss: 1.5347... Generator Loss: 0.6268
Epoch 2/5... Discriminator Loss: 1.8537... Generator Loss: 0.6485
Epoch 2/5... Discriminator Loss: 1.5495... Generator Loss: 0.5505
Epoch 2/5... Discriminator Loss: 1.5231... Generator Loss: 0.6163
Epoch 2/5... Discriminator Loss: 1.6978... Generator Loss: 0.5743
Epoch 2/5... Discriminator Loss: 1.8029... Generator Loss: 0.6510
Epoch 2/5... Discriminator Loss: 1.7678... Generator Loss: 0.6102
Epoch 2/5... Discriminator Loss: 1.5272... Generator Loss: 0.5704
Epoch 2/5... Discriminator Loss: 1.9330... Generator Loss: 0.5292
Epoch 2/5... Discriminator Loss: 1.8429... Generator Loss: 0.6039
Epoch 2/5... Discriminator Loss: 1.6385... Generator Loss: 0.7232
Epoch 2/5... Discriminator Loss: 1.8324... Generator Loss: 0.5968
Epoch 2/5... Discriminator Loss: 1.7469... Generator Loss: 0.5949
Epoch 3/5... Discriminator Loss: 1.6752... Generator Loss: 0.5761
Epoch 3/5... Discriminator Loss: 1.6733... Generator Loss: 0.5455
Epoch 3/5... Discriminator Loss: 1.5643... Generator Loss: 0.5539
Epoch 3/5... Discriminator Loss: 1.5335... Generator Loss: 0.6208
Epoch 3/5... Discriminator Loss: 1.5312... Generator Loss: 0.6055
Epoch 3/5... Discriminator Loss: 1.4181... Generator Loss: 0.5977
Epoch 3/5... Discriminator Loss: 1.8065... Generator Loss: 0.5970
Epoch 3/5... Discriminator Loss: 1.5741... Generator Loss: 0.6626
Epoch 3/5... Discriminator Loss: 2.1101... Generator Loss: 0.4892
Epoch 3/5... Discriminator Loss: 1.5916... Generator Loss: 0.6670
Epoch 3/5... Discriminator Loss: 1.6095... Generator Loss: 0.6534
Epoch 3/5... Discriminator Loss: 1.5556... Generator Loss: 0.5984
Epoch 3/5... Discriminator Loss: 1.3867... Generator Loss: 0.6893
Epoch 3/5... Discriminator Loss: 1.6715... Generator Loss: 0.5394
Epoch 3/5... Discriminator Loss: 1.7369... Generator Loss: 0.5784
Epoch 3/5... Discriminator Loss: 1.5602... Generator Loss: 0.5044
Epoch 3/5... Discriminator Loss: 1.7276... Generator Loss: 0.4894
Epoch 3/5... Discriminator Loss: 1.4902... Generator Loss: 0.6305
Epoch 3/5... Discriminator Loss: 1.6639... Generator Loss: 0.6172
Epoch 3/5... Discriminator Loss: 1.6469... Generator Loss: 0.6021
Epoch 3/5... Discriminator Loss: 1.8586... Generator Loss: 0.7626
Epoch 3/5... Discriminator Loss: 1.9778... Generator Loss: 0.6625
Epoch 3/5... Discriminator Loss: 1.5304... Generator Loss: 0.6645
Epoch 3/5... Discriminator Loss: 1.7100... Generator Loss: 0.6907
Epoch 3/5... Discriminator Loss: 1.5406... Generator Loss: 0.6002
Epoch 3/5... Discriminator Loss: 1.6397... Generator Loss: 0.6204
Epoch 3/5... Discriminator Loss: 1.6185... Generator Loss: 0.6053
Epoch 3/5... Discriminator Loss: 1.6473... Generator Loss: 0.6051
Epoch 3/5... Discriminator Loss: 1.5943... Generator Loss: 0.5242
Epoch 3/5... Discriminator Loss: 1.5893... Generator Loss: 0.5563
Epoch 3/5... Discriminator Loss: 1.5587... Generator Loss: 0.5845
Epoch 3/5... Discriminator Loss: 1.6591... Generator Loss: 0.6266
Epoch 3/5... Discriminator Loss: 1.6229... Generator Loss: 0.5581
Epoch 3/5... Discriminator Loss: 1.4758... Generator Loss: 0.6610
Epoch 3/5... Discriminator Loss: 1.4544... Generator Loss: 0.6462
Epoch 3/5... Discriminator Loss: 1.3731... Generator Loss: 0.6737
Epoch 3/5... Discriminator Loss: 1.6776... Generator Loss: 0.6255
Epoch 3/5... Discriminator Loss: 1.6033... Generator Loss: 0.6151
Epoch 3/5... Discriminator Loss: 1.5903... Generator Loss: 0.6029
Epoch 3/5... Discriminator Loss: 1.5414... Generator Loss: 0.6620
Epoch 3/5... Discriminator Loss: 1.6256... Generator Loss: 0.6332
Epoch 3/5... Discriminator Loss: 1.9009... Generator Loss: 0.5730
Epoch 3/5... Discriminator Loss: 1.7145... Generator Loss: 0.6350
Epoch 3/5... Discriminator Loss: 1.4685... Generator Loss: 0.5539
Epoch 3/5... Discriminator Loss: 1.4899... Generator Loss: 0.6598
Epoch 3/5... Discriminator Loss: 1.6198... Generator Loss: 0.5878
Epoch 3/5... Discriminator Loss: 1.5740... Generator Loss: 0.6196
Epoch 3/5... Discriminator Loss: 1.6398... Generator Loss: 0.5613
Epoch 3/5... Discriminator Loss: 1.5767... Generator Loss: 0.6117
Epoch 3/5... Discriminator Loss: 1.6530... Generator Loss: 0.5834
Epoch 3/5... Discriminator Loss: 1.6134... Generator Loss: 0.5844
Epoch 3/5... Discriminator Loss: 1.6681... Generator Loss: 0.5840
Epoch 3/5... Discriminator Loss: 1.5192... Generator Loss: 0.6268
Epoch 3/5... Discriminator Loss: 1.5027... Generator Loss: 0.6004
Epoch 3/5... Discriminator Loss: 1.5938... Generator Loss: 0.5176
Epoch 3/5... Discriminator Loss: 1.7286... Generator Loss: 0.6097
Epoch 3/5... Discriminator Loss: 1.7096... Generator Loss: 0.6570
Epoch 3/5... Discriminator Loss: 1.8186... Generator Loss: 0.5438
Epoch 3/5... Discriminator Loss: 1.6000... Generator Loss: 0.6321
Epoch 3/5... Discriminator Loss: 1.5878... Generator Loss: 0.5822
Epoch 3/5... Discriminator Loss: 1.5716... Generator Loss: 0.6471
Epoch 3/5... Discriminator Loss: 1.7185... Generator Loss: 0.6510
Epoch 3/5... Discriminator Loss: 1.6554... Generator Loss: 0.5818
Epoch 4/5... Discriminator Loss: 1.6333... Generator Loss: 0.6295
Epoch 4/5... Discriminator Loss: 1.6605... Generator Loss: 0.6474
Epoch 4/5... Discriminator Loss: 1.5911... Generator Loss: 0.5503
Epoch 4/5... Discriminator Loss: 1.7667... Generator Loss: 0.5602
Epoch 4/5... Discriminator Loss: 1.7823... Generator Loss: 0.6247
Epoch 4/5... Discriminator Loss: 1.4451... Generator Loss: 0.5896
Epoch 4/5... Discriminator Loss: 1.6692... Generator Loss: 0.5896
Epoch 4/5... Discriminator Loss: 1.6688... Generator Loss: 0.6280
Epoch 4/5... Discriminator Loss: 1.5546... Generator Loss: 0.6294
Epoch 4/5... Discriminator Loss: 1.6848... Generator Loss: 0.5698
Epoch 4/5... Discriminator Loss: 1.5782... Generator Loss: 0.6236
Epoch 4/5... Discriminator Loss: 1.5157... Generator Loss: 0.6153
Epoch 4/5... Discriminator Loss: 1.6191... Generator Loss: 0.6096
Epoch 4/5... Discriminator Loss: 1.6362... Generator Loss: 0.6617
Epoch 4/5... Discriminator Loss: 1.7503... Generator Loss: 0.5718
Epoch 4/5... Discriminator Loss: 1.9059... Generator Loss: 0.6157
Epoch 4/5... Discriminator Loss: 1.4728... Generator Loss: 0.5376
Epoch 4/5... Discriminator Loss: 1.4290... Generator Loss: 0.6780
Epoch 4/5... Discriminator Loss: 1.7979... Generator Loss: 0.4800
Epoch 4/5... Discriminator Loss: 1.4872... Generator Loss: 0.6624
Epoch 4/5... Discriminator Loss: 1.6455... Generator Loss: 0.7195
Epoch 4/5... Discriminator Loss: 1.6508... Generator Loss: 0.5862
Epoch 4/5... Discriminator Loss: 1.5725... Generator Loss: 0.5975
Epoch 4/5... Discriminator Loss: 1.6009... Generator Loss: 0.5122
Epoch 4/5... Discriminator Loss: 1.5800... Generator Loss: 0.6048
Epoch 4/5... Discriminator Loss: 1.6303... Generator Loss: 0.5641
Epoch 4/5... Discriminator Loss: 1.5372... Generator Loss: 0.5596
Epoch 4/5... Discriminator Loss: 1.4819... Generator Loss: 0.5940
Epoch 4/5... Discriminator Loss: 1.6300... Generator Loss: 0.5371
Epoch 4/5... Discriminator Loss: 1.6742... Generator Loss: 0.5983
Epoch 4/5... Discriminator Loss: 1.7288... Generator Loss: 0.6757
Epoch 4/5... Discriminator Loss: 1.5573... Generator Loss: 0.6634
Epoch 4/5... Discriminator Loss: 1.6523... Generator Loss: 0.6357
Epoch 4/5... Discriminator Loss: 1.6384... Generator Loss: 0.6084
Epoch 4/5... Discriminator Loss: 1.5956... Generator Loss: 0.5541
Epoch 4/5... Discriminator Loss: 1.5381... Generator Loss: 0.6233
Epoch 4/5... Discriminator Loss: 1.5003... Generator Loss: 0.6285
Epoch 4/5... Discriminator Loss: 1.7097... Generator Loss: 0.5608
Epoch 4/5... Discriminator Loss: 1.5242... Generator Loss: 0.6731
Epoch 4/5... Discriminator Loss: 1.5235... Generator Loss: 0.5967
Epoch 4/5... Discriminator Loss: 1.7209... Generator Loss: 0.6504
Epoch 4/5... Discriminator Loss: 1.6756... Generator Loss: 0.6530
Epoch 4/5... Discriminator Loss: 1.6933... Generator Loss: 0.5942
Epoch 4/5... Discriminator Loss: 1.6640... Generator Loss: 0.5674
Epoch 4/5... Discriminator Loss: 1.6367... Generator Loss: 0.6061
Epoch 4/5... Discriminator Loss: 1.6034... Generator Loss: 0.6535
Epoch 4/5... Discriminator Loss: 1.6235... Generator Loss: 0.5520
Epoch 4/5... Discriminator Loss: 1.4493... Generator Loss: 0.6220
Epoch 4/5... Discriminator Loss: 1.5731... Generator Loss: 0.5604
Epoch 4/5... Discriminator Loss: 1.4523... Generator Loss: 0.6037
Epoch 4/5... Discriminator Loss: 1.5459... Generator Loss: 0.6439
Epoch 4/5... Discriminator Loss: 1.6140... Generator Loss: 0.5318
Epoch 4/5... Discriminator Loss: 1.4465... Generator Loss: 0.5976
Epoch 4/5... Discriminator Loss: 1.5047... Generator Loss: 0.5692
Epoch 4/5... Discriminator Loss: 1.6756... Generator Loss: 0.5792
Epoch 4/5... Discriminator Loss: 1.5683... Generator Loss: 0.6559
Epoch 4/5... Discriminator Loss: 1.5085... Generator Loss: 0.6790
Epoch 4/5... Discriminator Loss: 1.5597... Generator Loss: 0.6424
Epoch 4/5... Discriminator Loss: 1.6264... Generator Loss: 0.5332
Epoch 4/5... Discriminator Loss: 1.6287... Generator Loss: 0.5542
Epoch 4/5... Discriminator Loss: 1.7060... Generator Loss: 0.5507
Epoch 4/5... Discriminator Loss: 1.6227... Generator Loss: 0.5654
Epoch 4/5... Discriminator Loss: 1.8068... Generator Loss: 0.6031
Epoch 4/5... Discriminator Loss: 1.5769... Generator Loss: 0.5475
Epoch 5/5... Discriminator Loss: 1.5194... Generator Loss: 0.5866
Epoch 5/5... Discriminator Loss: 1.6524... Generator Loss: 0.6108
Epoch 5/5... Discriminator Loss: 1.4483... Generator Loss: 0.6166
Epoch 5/5... Discriminator Loss: 1.6071... Generator Loss: 0.5926
Epoch 5/5... Discriminator Loss: 1.5976... Generator Loss: 0.6526
Epoch 5/5... Discriminator Loss: 1.5772... Generator Loss: 0.5829
Epoch 5/5... Discriminator Loss: 1.4769... Generator Loss: 0.7003
Epoch 5/5... Discriminator Loss: 1.5106... Generator Loss: 0.5425
Epoch 5/5... Discriminator Loss: 1.5887... Generator Loss: 0.6091
Epoch 5/5... Discriminator Loss: 1.4224... Generator Loss: 0.6120
Epoch 5/5... Discriminator Loss: 1.5366... Generator Loss: 0.6657
Epoch 5/5... Discriminator Loss: 1.6050... Generator Loss: 0.5604
Epoch 5/5... Discriminator Loss: 1.4118... Generator Loss: 0.6433
Epoch 5/5... Discriminator Loss: 1.6688... Generator Loss: 0.6438
Epoch 5/5... Discriminator Loss: 1.5400... Generator Loss: 0.5921
Epoch 5/5... Discriminator Loss: 1.4864... Generator Loss: 0.5784
Epoch 5/5... Discriminator Loss: 1.4187... Generator Loss: 0.5684
Epoch 5/5... Discriminator Loss: 1.6395... Generator Loss: 0.6111
Epoch 5/5... Discriminator Loss: 1.3923... Generator Loss: 0.6881
Epoch 5/5... Discriminator Loss: 1.3995... Generator Loss: 0.5660
Epoch 5/5... Discriminator Loss: 1.5104... Generator Loss: 0.6100
Epoch 5/5... Discriminator Loss: 1.5604... Generator Loss: 0.6582
Epoch 5/5... Discriminator Loss: 1.6309... Generator Loss: 0.6329
Epoch 5/5... Discriminator Loss: 1.4896... Generator Loss: 0.6582
Epoch 5/5... Discriminator Loss: 1.5817... Generator Loss: 0.5797
Epoch 5/5... Discriminator Loss: 1.5344... Generator Loss: 0.5614
Epoch 5/5... Discriminator Loss: 1.5258... Generator Loss: 0.4948
Epoch 5/5... Discriminator Loss: 1.4667... Generator Loss: 0.6663
Epoch 5/5... Discriminator Loss: 1.5552... Generator Loss: 0.6370
Epoch 5/5... Discriminator Loss: 1.4311... Generator Loss: 0.6120
Epoch 5/5... Discriminator Loss: 1.5334... Generator Loss: 0.7201
Epoch 5/5... Discriminator Loss: 1.4920... Generator Loss: 0.6515
Epoch 5/5... Discriminator Loss: 1.5079... Generator Loss: 0.5893
Epoch 5/5... Discriminator Loss: 1.5070... Generator Loss: 0.6510
Epoch 5/5... Discriminator Loss: 1.5149... Generator Loss: 0.6256
Epoch 5/5... Discriminator Loss: 1.5256... Generator Loss: 0.6360
Epoch 5/5... Discriminator Loss: 1.6986... Generator Loss: 0.5820
Epoch 5/5... Discriminator Loss: 1.5595... Generator Loss: 0.5758
Epoch 5/5... Discriminator Loss: 1.6076... Generator Loss: 0.6435
Epoch 5/5... Discriminator Loss: 1.5995... Generator Loss: 0.5780
Epoch 5/5... Discriminator Loss: 1.4954... Generator Loss: 0.6531
Epoch 5/5... Discriminator Loss: 1.5777... Generator Loss: 0.5807
Epoch 5/5... Discriminator Loss: 1.6681... Generator Loss: 0.5389
Epoch 5/5... Discriminator Loss: 1.7126... Generator Loss: 0.6747

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.